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» Variable selection using neural-network models
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EOR
2007
85views more  EOR 2007»
14 years 9 months ago
Reject inference, augmentation, and sample selection
Many researchers see the need for reject inference in credit scoring models to come from a sample selection problem whereby a missing variable results in omitted variable bias. Al...
John Banasik, Jonathan Crook
SDM
2009
SIAM
202views Data Mining» more  SDM 2009»
15 years 7 months ago
Proximity-Based Anomaly Detection Using Sparse Structure Learning.
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe,...
NCA
2002
IEEE
14 years 9 months ago
The Construction of Smooth Models using Irregular Embeddings Determined by a Gamma Test Analysis
One of the key problems in forming a smooth model from input-output data is the determination of which input variables are relevant in predicting a given output. In this paper we ...
Alban P. M. Tsui, Antonia J. Jones, A. Guedes de O...
JMLR
2010
159views more  JMLR 2010»
14 years 4 months ago
Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks
Networks are becoming a unifying framework for modeling complex systems and network inference problems are frequently encountered in many fields. Here, I develop and apply a gener...
Nikolai Slavov
ESANN
2000
14 years 11 months ago
Load forecasting dealing with medium voltage network reconfiguration
Planing the operation in modern power systems requires suitable anticipation of load evolution at different levels of distribution network. Under this perspective, load forecasting...
José Nuno Fidalgo, João Abel Pe&cced...